In recent years, driven by the COVID-19 pandemic, the pharma industry has undergone increased levels of digital transformation. The availability of ultra-large datasets and technological advances has led to more interest in the use of artificial intelligence (AI) and big data analytics across the pharma value chain, from drug discovery and clinical trial design, right through to sales and marketing.
Over the past 3-4 years, there has been increased interest in the use of AI in drug discovery, as witnessed by the emergence of an ever-growing number of start-ups operating in this area, increasing number of drug discovery partnerships, and record levels of investment. While most drugs developed using AI are in the early stages of development, there have been some recent major milestones, including the first drug developed by AI to enter clinical trials and the repurposing of an already marketed drug to treat COVID-19.
The artificial intelligence in drug discovery thematic intelligence report assesses provides an overview of the current landscape, including healthcare, technology, regulatory, and macroeconomic trends, as well as key players, while also highlighting opportunities for the use of AI in the future. Furthermore, it provides an industry-specific analysis based on GlobalData databases and surveys, as well as several case studies.
The key trends that will shape the AI in drug discovery theme can be classified into four categories: healthcare trends, technology trends, macroeconomic trends, and regulatory trends.
- Healthcare trends – The key healthcare trends that will shape the AI in drug discovery theme are COVID-19 on digital transformation in pharma, the rising cost of R&D and dwindling pipelines, increasing availability of ultra-large datasets, increased need for open science in drug discovery, pharma companies building in-house AI capabilities, formation of industry consortia in AI, and role of AI in precision and personalized medicine.
- Technology trends – The report focuses on key technology trends impacting AI in drug discovery theme including the role of technology giants in AI-based drug discovery, big data, cloud, quantum computing, and cybersecurity.
- Macroeconomic trends – The key macroeconomic trends that will shape the AI in drug discovery theme are addressing AI skills shortages, increased AI partnerships in drug discovery, increased volume and value of AI-related funding deals in drug discovery, mergers and acquisitions (M&As), and China’s quest for AI dominance.
- Regulatory trends – The report highlights the key regulatory trends shaping the AI in drug discovery theme including the International Coalition of Medicines Regulatory Authorities (ICMRA) report on the use of AI to develop drugs, regulatory divergence, the European Commission (EC) white paper, and the proposed framework on AI, and the US leadership in AI.
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AI in Drug Discovery – Industry Analysis
The pharma industry is slow when adopting new technologies such as AI. However, there has been increased activity over the past few years. Many more companies, including recognized leaders, are adopting AI as part of their digital transformation strategies. AI will be a key driver of healthcare innovation in the future through various applications that include management of chronic diseases, drug discovery and development, improvement of clinical trials, manufacturing, and supply chains. COVID-19 has been one of the reasons for the rapid innovation and investment in AI. Compared to pharma and medical device companies, GlobalData forecasts that healthcare providers will spend the most on AI platforms in 2024.
The AI in drug discovery market analysis also covers:
- Analysis of drugs discovered using AI
- Survey data on the adoption of AI in pharma
- Case studies
- Hiring trends
- Company filing trends
- Social media trends
Global AI Platform Revenue in Pharma, Medical, and Healthcare, 2019-2024
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AI in Drug Discovery - Value Chain Analysis
The main applications of AI algorithms in drug discovery are the identification and validation of drug targets, virtual screening of compounds, de novo drug design, drug repurposing, and identification of treatment response biomarkers.
Drug discovery begins with the identification of drug targets, which are molecules that are inherently linked to a particular disease process. They should exhibit several features such as involvement in a crucial biological pathway, functionally and structurally characterized, and the ability to interact with drug-like compounds.
AI in Drug Discovery Value Chain
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Leading AI Technology Vendors
Some of the leading technology vendors within the AI in drug discovery theme are:
Leading AI Specialists in Drug Discovery
Some of the leading AI specialists in drug discovery are:
Leading Pharma Adopters of AI in Drug Discovery
Some of the leading pharma adopters of AI in drug discovery are:
- Bristol Myers Squibb
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AI in Drug Discovery Market Overview
|Key Trends||Healthcare Trends, Technology Trends, Macroeconomic Trends, and Regulatory Trends|
|Value Chains||Target Identification and Validation, Generation of Molecule Leads/De Novo Drug Design, Drug Repurposing, and Response Biomarker Discovery|
|Leading AI Technology Vendors||Microsoft, Alphabet, and IBM among others|
|Leading Specialist AI Vendors||AbCellera, Atomwise, and Auransa among others|
|Leading Pharma adopters||AstraZeneca, Bristol Myers Squibb, and GSK among others|
Reasons to Buy
- See who the leading players are in the AI in drug discovery space.
- See how the competitive landscape is evolving, with a review of company activity including strategic partnerships and funding deals, as well as mergers and acquisitions (M&A).
- See what trends are driving the use of AI in drug discovery.
- See an analysis of drugs discovered by AI, including by company, phase of development, therapy area, and molecule type.
- Insilico Medicine
- Recursion Pharmaceuticals
- Lantern Pharma
- International Business Machines (IBM)
Table of Contents
• Healthcare Trends
• Technology Trends
• Regulatory trends
• Macroeconomic Trends
• Market size and growth forecasts
• Analysis of drugs discovered using AI
• Survey data on the adoption of AI in pharma
• Case studies
• Hiring trends
• Company filings trends
• Social media trends
• Target identification and validation
• Generation of molecule leads/de novo drug design
• Drug repurposing
• Response biomarker discovery
• Leading AI technology vendors
• Specialist AI vendors in drug discovery
• Leading pharma adopters of AI in drug discovery
• Further Reading
• About the Authors
• Our Thematic Research Methodology
• About GlobalData
• Contact Us
List of Tables
Table 1: Healthcare trends impacting AI in drug discovery
Table 2: Technology trends impacting AI in drug discovery
Table 3: Macroeconomic trends impacting AI in drug discovery
Table 4: Regulatory trends impacting AI in drug discovery
Table 5: Examples of drugs in clinical development by highest phase of development
Table 6: Top 20 pharma partnerships in AI-based drug discovery by value
Table 7: Examples of top VC deals associated with AI in drug discovery
Table 8: Examples of M&A deals associated with AI in drug discovery
Table 9: Examples of publicly available platforms and databases used for target identification
Table 10: Examples of AI technologies used for target identification
Table 11: Examples of companies with technology for generation of molecule leads and de novo drug design
List of Figures
Figure 1: Examples of leading players in AI in drug discovery and where do they sit in the value chain?
Figure 2: Key components of machine learning
Figure 3: Global AI platform revenue in pharma, medical, and healthcare, 2019–24
Figure 4: Top companies by number of drugs developed using AI-based technologies
Figure 5: Breakdown of drugs by highest phase of development
Figure 6: Breakdown of drugs by therapy area
Figure 7: Breakdown of drugs by molecule type
Figure 8: Role of AI in optimizing drug discovery and development
Figure 9: Current and expected use of AI in drug discovery and development
Figure 10: Most pharma companies will use AI vendors to implement the technology across their value chain
Figure 11: Impact of the COVID-19 pandemic on investment in AI
Figure 12: Technologies pharma is prioritizing for current investments
Figure 13: Investment in emerging technologies over the next two years
Figure 14: Use of AI in drug discovery and development is expected to peak in more than nine years
Figure 15: Number of AI-based drug discovery strategic alliances has increased since 2015
Figure 16: Top AI vendors by number of deals, 2015–22
Figure 17: Top pharma companies by number of AI drug discovery deals, 2015–22
Figure 18: Number and value of AI-based drug discovery VC deals has increased since 2015
Figure 19: AI job postings in pharma, 2019–22
Figure 20: Number of AI mentions in company filings, 2016–22
Figure 21: Top influencer trends related to AI
Figure 22: Top influencer posts related to AI and drug discovery, 2019–22
Figure 23: AI in drug discovery value chain
Figure 24: Examples of leaders and challengers in target identification and validation
Figure 25: Computer-aided drug discovery methods
Figure 26: Examples of leaders and challengers in molecule lead generation and de novo drug design
Figure 27: Examples of leaders and challengers in drug repurposing
Figure 28: Examples of leaders and challengers in response biomarker discovery
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